摘要：
GlobalAveragePooling2D层keras.layers。pooling GlobalAveragePooling2D（dim_ordering='default'）将全局平均池参数数据应用于空域信号_格式：字符串，“channels_first”或“channels-last”之一，表示图像通道维度的位置。此参数是Keras1.x_dim_orden中的图像
GlobalAveragePooling2D层
keras.layers.pooling.GlobalAveragePooling2D(dim_ordering=‘default‘)
为空域信号施加全局平均值池化
参数
- data_format：字符串，“channels_first”或“channels_last”之一，代表图像的通道维的位置。该参数是Keras 1.x中的image_dim_ordering，“channels_last”对应原本的“tf”，“channels_first”对应原本的“th”。以128x128的RGB图像为例，“channels_first”应将数据组织为（3,128,128），而“channels_last”应将数据组织为（128,128,3）。该参数的默认值是
~/.keras/keras.json中设置的值，若从未设置过，则为“channels_last”。
输入shape
‘channels_first’模式下，为形如（samples，channels, rows，cols）的4D张量
‘channels_last’模式下，为形如（samples，rows, cols，channels）的4D张量
输出shape
形如(nb_samples, channels)的2D张量
示例代码
keras-finetuning
def build_model(nb_classes): base_model = InceptionV3(weights='imagenet', include_top=False) # add a global spatial average pooling layer x = base_model.output x = GlobalAveragePooling2D()(x) # let's add a fully-connected layer x = Dense(1024, activation='relu')(x) # and a logistic layer predictions = Dense(nb_classes, activation='softmax')(x) # this is the model we will train model = Model(input=base_model.input, output=predictions) # first: train only the top layers (which were randomly initialized) # i.e. freeze all convolutional InceptionV3 layers for layer in base_model.layers: layer.trainable = False # compile the model (should be done *after* setting layers to non-trainable) print "starting model compile" compile(model) print "model compile done" return model
Kaggle-Sea-Lions-Solution
def get_model(): input_shape = (image_size, image_size, 3) model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3), padding='same', input_shape=input_shape)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(n_classes, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(GlobalAveragePooling2D()) print (model.summary()) #sys.exit(0) # model.compile(loss=keras.losses.mean_squared_error, optimizer= keras.optimizers.Adadelta()) return model